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Journal of Agricultural Education, 55(3), 17-31.
doi: 10.5032/jae.2014.03017
17
Extension Youth Educators’ Technology Use in Youth
Development Programming
Carli McClure1, Brittany Buquoi2, Joe W. Kotrlik3, Krisanna Machtmes4 and J.C. Bunch5
Abstract
The purpose of this descriptive-correlational study was to determine the use of technology in
youth programming by Extension youth development educators in Louisiana, Mississippi, and
Tennessee. Data were collected via e-mail and a SurveyMonkey© questionnaire. Extension
educators are using some technology in youth development programming. More than three-
fourths of Extension youth educators are using Facebook; however, less than one fourth of
Extension youth educators are using Twitter which contradicts previous research. Having
technology available for use explains a medium amount of the technology use among Extension
youth educators; however, perceived barriers, anxiety, age, gender, years of experience, and
sources of technology training do not explain Extension youth educators technology use in their
programming. Extension youth educators fit the description of digital immigrants who assume
that today’s learners acquire knowledge the same way they learned when they were in school.
Keywords: extension, youth development, technology
Over the past decade, our society has become increasingly dependent on technology.
Since 2000, the rapid advances in technology have taken our society from being primarily
dependent on the written and spoken word to being more dependent on communication through
the air, including e-mail, text messaging, and the World Wide Web (Prensky, 2001a, 2001b). In
fact, the introduction of the Internet has not only changed the home, but it has also changed the
workplace and educational settings (Fraze, Fraze, Kieth, & Baker, 2002; Lokken, Cheek, &
Hastings, 2003). Prensky (2001a) coined the term digital native as those who grew up with and
are native speakers of the language of technology. Digital natives prefer interaction and
communication via digital technologies (Prensky, 2001a). Conversely, digital immigrants were
introduced to and adopted technology at a later point in their life. Further, digital immigrants
assume that today’s learners acquire knowledge the same way students did when they were in
school (Bennett, Maton, & Kervin, 2008; Prensky, 2001a).
However, technology has changed students’ learning techniques and interests. As a
result, teachers must be willing to adapt to these changes to better serve today’s learners (Bennett
et al., 2008; Prensky, 2001a, 2001b; Rhoades, Thomas, & Davis, 2009). Dooley and Murphy
1 Carli Mcclure is a doctoral student in the School of Human Resource Education and Workforce
Development at Louisiana State University, 298 Coates Hall, Baton Rouge, LA 70803, Email:
[email protected]. 2 Brittany Buquoi is a Director of Psychological Health at Minnesota Army National Guard, 8180 Belden
Blvd., Cottage Grove, MN 55016, Email: [email protected]. 3 Joe W. Kotrlik is Professor Emeritus in the School of Human Resource Education and Workforce
Development at Louisiana State University, Baton Rouge, LA, 70803, Email: [email protected]. 4 Krisanna Machtmes is an Associate Professor of Educational Studies, Patton College of Education |
McCracken Hall, Ohio University, Athens, OH 45701-2979. Email: [email protected] 5 J.C. Bunch is an Assistant Professor of Agricultural Education in the School of Human Resource
Education and Workforce Development at Louisiana State University, 283 Coates Hall, Baton Rouge, LA
70803, Email: [email protected].
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(2001) found that educators valued technology and believed that it played a significant role in the
learning process. Further, educators believed that education could be improved by increasing
technology use because learners prefer learning to be more visual; however, they did not believe
that technology will impact the information that is taught (Dooley & Murphy, 2001).
Although using technology may save time and appeal to today’s learners, educators must
have a clear understanding of how to use technology efficiently and effectively in the teaching
and learning process (TLP) to provide the best learning experience for learners (Peckham &
Iverson, 2000). Like formal educators, Extension educators must be willing to learn about the
appropriate uses of technology, methods by which technology can make programming more
efficient, and the long-term benefits of using technology in their programming (Seger, 2011).
Further, Extension administrators should encourage and emphasize the importance of technology
use if Extension educators at the local level are to be successful at integrating technology into
their programming (Diem, Hino, Martin, & Meisenbach, 2011; Seger, 2011).
While Extension has traditionally conducted programs face-to-face, Gregg and Irani
(2004) suggested that Extension educators are experiencing a shift in the way they carry out their
duties due to the technology avenues that are available. In a study that focused on agricultural
and Extension education faculty members’ use of technology, Flood and Conklin (2003) found
that Extension educators believed that learners and educators alike are positively impacted by the
use of technology in the learning process. In fact, a majority of the Extension educators studied
indicated that they were using computers, email, Internet, and presentation software to reach
clientele in Extension programming (Gregg & Irani, 2004). Additionally, Dooley and Murphy
(2001) and Peake, Briers, and Murphy (2005) found that educators felt confident using basic
technology in the educational process including Internet, email, word processing, presentation
graphics, projection devices, and videoconferencing. Delivering Extension programming online
offers flexibility to clientele because they can access educational information at their own
convenience (Green, 2012). Further, use of webinars to deliver Extension programming offered a
multitude of benefits including cost-effective and time-efficient programming, easier
collaboration among Extension personnel and clientele, and opportunity to reach new clientele
with programming (Rich et al., 2011). On the other hand, Extension educators were less
confident in their ability to create and maintain their own webpages, record digital sound into
presentations, and teach courses via the web (Dooley & Murphy, 2001; Peake et al., 2005).
In regard to newer technologies, O’Neill, Zumwalt, and Bechman (2011) found that a
majority of Extension educators were using Facebook, Twitter, and YouTube on their phones and
computers to conduct Extension programming. In fact, horticulture lessons provided by
Extension educators via Facebook were found to engage learners, assist in achieving learning
objectives, stimulate students’ recollection of previous lessons, and encourage active learning
among students (Strong & Alvis, 2011). Further, Extension educators indicated that using
Facebook as a learning tool encouraged collaboration among learners and educators (Rhoades et
al., 2009; Strong & Alvis, 2011). Because so many adults and youth use Facebook on a daily or
almost daily basis (Van Grove, 2010), using Facebook as a tool to reach new clientele and
communicate with existing clientele can save time and money (Rhoades et al., 2009; Robideau &
Santl, 2011; Strong & Alvis, 2011).
Although Extension educators have adopted technology in their programming to a
degree, Extension educators still perceived several barriers to integrating technology into the TLP
including: (a) sufficient time to integrate technology into lessons, (b) adequate technology to
accommodate all students, (c) accessibility of technical support, (d) lack of money to purchase
technology, and (e) planning ample time for learners to use technology (Diem et al., 2009; Kotrlik
& Redmann, 2005; Redmann & Kotrlik, 2004). In addition, Extension educators indicated that
the lack of time to learn how to use technology in education and limited training opportunities
were also barriers to incorporating technology into the TLP (Diem et al., 2011; Flood & Conklin,
2003). Further, Extension educators are not fully aware of the functionality of technology;
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therefore, they are hesitant to incorporate technology into their programming (Diem et al., 2011;
Seger, 2011). Extension educators also fear that using technology in their programming could
diminish their own presence of teaching which could potentially turn away existing clientele
(Diem et al., 2011; Seger, 2011). The common perception of Extension programming has shown
to be a barrier to adopting technology as older clientele view programming primarily as an in-
person event and younger clientele view programming as in person, online, and via social media
(Rhoades et al., 2009; Seger, 2011). Drill (2012) suggested that Extension educators may be
hesitant to use mobile technology to deliver Extension programming because it could potentially
lessen instructional time with youth. Consequently, as the presence of perceived barriers
increases, the adoption of technology decreases (Kotrlik & Redmann, 2005, 2009). Moreover,
keeping up with new technology requires constant innovation and quick implementation, and
Extension as an organization does not readily enable such innovation (Seger, 2011).
Seger (2011) suggested a way to reach both older and younger clientele in a way that is
satisfying. Extension educators should use “high tech and high touch” methods to deliver
programming (p. 5). Further, Extension educators could deliver newsletters and other monthly
news via social media avenues while maintaining regular face-to-face contact with clientele.
Therefore, Extension educators are still able to meet with their traditional clientele, but they also
have the potential to reach hundreds to thousands more with an online presence (Seger, 2011).
While making an effort to reduce the presence of barriers, it is important to consider that
educators experience a degree of anxiety toward technology use in the TLP (Kotrlik & Redmann,
2005, 2009; Redmann & Kotrlik, 2004). Research has shown that as anxiety toward technology
increased, technology use in the TLP decreased (Fraze et al., 2002; Kotrlik & Redmann, 2005,
2009; Lokken, Cheek, & Hastings, 2003). Further, as educators’ age increased, anxiety toward
technology increased and technology use decreased (Fraze et al., 2002; Lokken et al., 2003).
Modern learners “. . . approach learning as a ‘plug-and-play’ experience: they are
unaccustomed and unwilling to learn sequentially – to read the manual – and instead are inclined
to plunge in and learn through participation and experimentation” (Flood & Conklin, 2003,
p. 283). Accordingly, if educators want to be successful in reaching learners, they must realize
that technology has become the reality of today (Lokken et al., 2003). Further, Extension
educators must not only be willing to learn students’ digital language, but also incorporate
technology into teaching required competencies (Lokken et al., 2003; Prensky, 2001a, 2001b).
Using technology in the TLP can produce students who are more motivated, productive, and
empowered (Peckham & Iverson, 2000). Extension’s mission is to take education to people. A
majority of people today, especially those who are part of the digital generation, are online and
using social media; therefore, to reach these clientele, Extension educators must be willing to take
Extension programming to those avenues (Seger, 2011).
Theoretical Framework
The theoretical framework used for this study was Rogers’ (2003) diffusion of innovation
theory. An innovation is described as any object, practice, or idea that is perceived as new
(Rogers, 2003). Innovation-decision is a process beginning when an individual first gains
knowledge about an innovation. The individual then develops an attitude toward the innovation
that leads to adoption or rejection of the innovation. Furthermore, the process continues with the
individual implementing and confirming the innovation (Rogers, 2003). For this study, the
process is operationalized through Extension educators’ use of technology in youth development
programming. Having an understanding of the Extension educators’ stage in the process is
helpful in encouraging their adoption of technology in youth development programming
(Murphrey, Miller, & Roberts, 2009).
Rogers (2003) described five attributes that contributed to adoption of an innovation: (a)
relative advantage, (b) compatibility, (c) complexity, (d) trialability, and (e) observability.
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Relative advantage is described as the degree to which an individual perceives an innovation as
having an advantage over its predecessor (Rogers, 2003). If Extension educators perceive using
technology in youth development programming is more advantageous than more traditional
methods of delivery, technology will more likely be adopted in youth programming.
Compatibility is described by Rogers (2003) as the degree to which an individual perceives an
innovation to be compatible with his or her values, past experiences, and future needs. Extension
educators’ adoption of technology indicates a belief that technology is compatible with the
educators’ goals and needs for youth development programming.
Rogers (2003) describes complexity as the degree to which an individual perceives an
innovation as too challenging to comprehend and use. The presence of perceived barriers toward
using technology in youth development programming could present a sense of complexity to
Extension educators. If Extension educators perceive lack of technical support, lack of age-
appropriate instructional software, and limited ability to integrate technology into various types of
programs, the educator may perceive technology as too complex to adopt into programming.
Rogers (2003) described trialability as the process by which an innovation can be investigated on
a trial basis and observability as the time when individuals can see results of an innovation.
Often, individuals feel a sense of anxiety regarding technology use in the learning process
(Kotrlik & Redmann, 2005, 2009; Redmann & Kotrlik, 2004). Providing Extension educators
trialability and observability with technology could potentially reduce anxiety in the learning
process. The availability of technology and technology training can also reduce Extension
educators’ anxiety toward technology use. Extension educators who have the option to try and
observe technology through (a) workshops, (b) webinars, (c) college courses, and (d) other
colleagues may be more likely to feel less anxiety toward technology and, subsequently, adopt
technology into youth development programming. Therefore, Rogers’ (2003) theory indicates
that Extension educators who have the option of trialability and observability and believe that
using technology has relative advantage and compatibility with their goals in youth programming
will be more likely to adopt technology into their programming.
Purpose and Research Questions
The purpose of this descriptive-correlational study was to measure Extension youth
development educators’ use of technology in youth programming. This research study addresses
Research Priority 2, “New Technologies, Practices and Products Adoption Decisions,” of the
American Association for Agricultural Education Research Agenda for 2011-2015 (Doerfert,
2011). Measuring Extension youth educators’ technology use in youth development
programming will assist researchers in providing opportunities for Extension youth educators to
learn about new educational technologies which will facilitate in more effective programming for
youth. Six research questions guided this study:
1. What were the personal and professional characteristics of Extension youth educators
(e.g., age, gender, years of professional experience, sources and types of technology
training, and current technologies used in youth development programming)?
2. To what extent did Extension youth educators use technology in youth development
programming?
3. To what extent did Extension youth educators perceive selected factors as barriers to
using technology in youth development programming?
4. To what extent were Extension youth educators experiencing anxiety toward using
technology in youth development programming?
5. What relationships existed between Extension youth educators’ use of technology in
youth development programming and the following variables: perception of barriers to
using technology, level of perceived anxiety toward technology, and selected personal
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and professional characteristics (age, gender, years of professional experience, and
sources of technology training)?
6. Did selected variables explain a statistically significant proportion of the variance in
technology use by Extension youth educators? The variables used in this analysis were
technology used by respondents, barriers to technology use, anxiety toward technology
use, age, gender, years of professional experience, and sources and types of technology
training.
Methods
Population and Sample
The target population for this study included Extension youth educators in Louisiana,
Mississippi, and Tennessee (N = 308). These states were selected because of their similarities in
youth development programs. A random sample of Extension youth educators was selected using
Cochran’s (1977) sample size formula (n = 190). Eight of the 190 Extension educators in the
random sample were removed from the study as a result of frame error; the revised population
was 300 and the revised sample size was 182. Responses were collected from 130 of the 182
Extension youth educators for a response rate of 71.43%. Five of the respondents provided
incomplete data and three were determined to be outliers; therefore, there were 122 usable
responses which represents a 67.03% usable response rate.
Instrumentation
The instrument used in this study was developed by Kotrlik, Redmann, and Douglas
(2003). The original instrument was used to measure the technology adoption and availability,
technology training, barriers to integrating technology, teaching effectiveness, anxiety toward
technology use of agriscience teachers in the classroom. The researchers were given permission
to use the instrument. The items in the instrument were modified to properly measure technology
use by Extension youth educators in youth development programming, associated barriers to
using technology, and perceived anxiety toward using technology. In addition, the instrument
was further modified for online data collection via SurveyMonkey©.
A panel of experts composed of faculty members and doctoral level graduate students
reviewed the instrument for face and content validity and revised based on the results of the
review. Then, the instrument was pilot-tested with 50 Extension youth educators in Arkansas.
Following the instrument review and pilot test, necessary revisions were made to the instrument,
including changes to wording of items and instructions and omitting similar items.
The instrument contained 37 items and measured three constructs. A five point
summated scale (1 = Not like me, 2 = Very little like me, 3 = Some like me, 4 = Very much like me,
and 5 = Exactly like me) measured the first construct which contained 18 items that assessed
participants’ technology use in youth development programming. Additionally, an item was
added for participants to select among 12 types of technology that are available for use in youth
development programming. A four-point summated scale (1 = Not a barrier, 2 = Minor barrier,
3 = Moderate barrier, and 4 = Major barrier) measured the second construct which contained
seven items that evaluated participants’ perceptions of the magnitude of selected barriers to
integrating technology into youth development programming. A five-point summated scale (1 =
No anxiety, 2 = Some anxiety, 3 = Moderate anxiety, 4 = High anxiety, and 5 = Very high anxiety)
measured the third construct which contained seven items that gauged participants’ perceived
level of anxiety towards using technology in the TLP. Additionally, the instrument contained
four items that allowed participants to indicate their age, gender, years of professional experience,
and sources and types of technology training.
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The reliability of the scales for the constructs measured in this study were analyzed ex
post facto using Cronbach’s alpha coefficients. The reliability analysis yielded exemplary
Cronbach’s alpha coefficients according to the standards published by Robinson, Shaver and
Wrightsman (1991): technology use α = .92; barriers α = .82; and anxiety α = .95.
Data Collection
The researchers collected responses from the target population using Dillman, Smyth,
and Christian’s (2008) Tailored Design Method. All participants were contacted via a
SurveyMonkey© email that described the purpose of the study and contained a link to the
questionnaire. The non-respondents at the end of weeks one and two were contacted via
SurveyMonkey© email. At the end of week three, a random sample of the remaining
non-respondents were contacted via telephone to control for non-response error. To ensure that
the results were representative of the target population, an independent samples t-test was used to
compare respondents and non-respondents. No statistically significant differences were found for
key variables between the respondent and non-respondent groups; therefore, data were combined
for further analysis.
Data Analysis
The data analyses for research questions one through four involved computing
descriptive statistics (e.g., means, percentages, frequencies, and standard deviations). Research
question five was analyzed using Pearson product-moment correlation coefficients. Pearson r
offers a significant index for demonstrating relationship (Ary, Jacobs, Sorensen, & Razavieh,
2010). The strength of relationships was determined using Davis’ (1971) coefficient conventions:
r = .01 to .09 = Negligible, r = .10 to .29 = Low, r = .30 to .49 = Moderate, r = .50 to .69 =
Substantial, and r ≥ .70 = Very Strong. Research question six was analyzed using forward
multiple regression analysis. According to Ary et al. (2010), multiple regression identifies
relationships among several variables, allowing researchers to find the most significant
correlation between independent and dependent variables. A statistical significance level of .05
was established a priori for all statistical tests.
Findings
Research question one sought to describe the Extension educators’ personal and
professional characteristics (N = 122). Of the respondents, 43 (35.2%) were male and 79 (64.8%)
were female. The respondents’ mean age was 39 years of age (SD = 10.6), and the mean years of
experience among respondents was 11 (SD = 9.2). Regarding sources of technology training, a
majority of respondents (116, 95%) indicated being self-taught, and over three-fourths (106,
87%) self-selected workshops and conferences as a source of training. Further, two-thirds (85,
70%) of respondents reported the use of colleagues, over one-third (57, 47%) indicated college
courses, more than one-fourth (41, 34%) identified webinars, and less than one-fourth (23, 19%)
reported eXtension as sources of training.
Extension educators were asked to select which technologies they used in youth
development programming from a provided list of 12 technologies. More than three-fourths of
respondents indicated use of the first four technologies in youth development programming as
shown in Table 1, digital photo cameras (f = 110, 90%), text messaging (f = 110, 90%), Facebook
(f = 106, 87%) and DVD or CD players (f = 92, 76%). Nearly three-fourths of the respondents
identified use of smart phones (f = 90, 71%), and more than one-third of respondents documented
using digital video cameras (f = 47, 39%) in youth development programming. The remaining six
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technologies listed were reported to be used by less than one-third of Extension youth educators
(see Table 1).
Table 1
Technology Used by Extension Youth Educators in Youth Development Programming
Instructional Technologies # %
Digital photo camera 110 90.2
Text messaging 110 90.2
Facebook 106 86.9
DVD or CD players 92 75.4
Smart phone (e.g., Phone, Android, Blackberry, etc.) 87 71.3
Digital video camera 47 38.5
Tablet computer (e.g., iPad, Galaxy Tab, Xoom, etc.) 34 27.9
CD or DVD recorder 31 25.4
Console gaming (e.g., Playstation, Wii, Xbox, Nintendo DS, etc.) 19 15.6
Twitter 19 15.6
Desktop computer gaming 11 9.0
Simulations (e.g., Second Life, etc.) 7 5.7
Note. N = 122. Number of technologies used: M = 5.52, SD = 1.68.
Research question two sought to determine what extent youth educators were using
technology in youth development programming. This construct was measured using a five-point
summated rating scale. The real limits of the scale were 1.00 to 1.49 = Not like me, 1.50 to 2.49 =
Very little like me, 2.50 to 3.49 = Some like me, 3.50 to 4.00 = Very much like me, and 4.00 to
4.50 = Exactly like me. Data are reported using summated means by item and construct (see
Table 2). A summated mean for all technology use items was calculated which yielded a
composite mean of 3.38 (SD = .56), indicating that Extension educators perceived items in the
technology use scale as “Some” like them. When technology use items were analyzed,
respondents rated “I use projection devices to give presentations, demonstrations, or lectures”
highest (M = 4.03, SD = .81). Extension educators rated “Technology allows me to be a
facilitator of learning rather than the source of all learning” as the second highest (M = 3.64, SD =
.74), and the third highest rated item was “I expect youth to use technology so they can take on
new challenges beyond traditional projects” (M = 3.57, SD = .74). However, the item with the
lowest mean as indicated respondents was “I encourage youth to design their own technology-
based projects” (M = 2.77, SD = .85) (see Table 2).
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Table 2
Technology Use in Youth Development Programming by Extension Youth Educators
Item M SD
I use projection devices to give presentations, demonstrations, or lectures. 4.03 .81
Technology allows me to be a facilitator of learning rather than the source of
all learning.
3.64 .74
I expect youth to use technology so they can take on new challenges beyond
traditional projects.
3.57 .74
I encourage youth to use technology to collaborate with other youth. 3.52 .85
I encourage youth to use the computer to do project-area learning activities. 3.52 .89
I encourage youth to use online multimedia resources (such as video, audio,
or interactive programs.)
3.48 .86
I expect youth to use technology to enable them to be self-directed learners. 3.48 .73
I expect youth to use technology so they develop projects that are of a higher
quality level than would be possible without them using technology.
3.40 .85
I pursue innovative ways to incorporate technology into programming with
youth.
3.38 .88
I use technology to encourage youth to share the responsibility for their own
learning.
3.36 .89
I emphasize the use of technology as a learning tool in my programs. 3.34 .79
I encourage youth to use various digital devices, smart phones, or tablet
computers in projects.
3.34 .98
I use technology as a standard learning tool for youth. 3.33 .73
I expect youth to fully understand the unique role that technology plays in
their learning.
3.25 .87
I often encourage youth to use e-mail to contact experts about projects. 3.24 1.03
I discuss with youth how they can use technology as a learning tool. 3.19 .83
I design educational programs that result in youth being comfortable using
technology in their learning.
3.07 .88
I encourage youth to design their own technology-based projects. 2.77 .85
Composite Mean: 3.38 .56
Note. (N = 122). 1 = Not like me, 2 = Very little like me, 3 = Some like me, 4 = Very much like me,
and 5 = Exactly like me. Scale interpretation: 1.00 to 1.49 = Not like me, 1.50 to 2.49 = Very
little like me, 2.50 to 3.49 = Some like me, 3.50 to 4.49 = Very Much Like Me, and 4.50 to 5.00 =
Exactly Like Me.
Research question three sought to determine what extent Extension youth educators
perceived selected factors as barriers to using technology in youth development programming.
The items in this scale were measured using a four-point summated rating scale. The real limits
of this scale were 1.00 to 1.49 = Not a barrier, 1.50 to 2.49 = Minor barrier, 2.50 to 3.49 =
Moderate barrier, and 3.50 to 4.00 = Major barrier. The summated mean for all items in the
barrier scale was 2.38 (SD = .56), which indicates that Extension educators perceive the factors in
this scale to be “Not a barrier” (Table 3). Extension youth educators reported that “Availability
of technology for the number of youth participating in my programs” (M = 2.91, SD = .79) and
“Availability of technical support to effectively use instructional technology in my programs” (M
= 2.65, SD = .84) were moderate barriers. The remaining five barriers listed were rated as minor
barriers (see Table 3).
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Table 3
Extension Youth Educators’ Perceived Barriers to Using Technology in Youth Development
Programming
Item M SD
Availability of technology for the number of youth participating in my
programs
2.91 .79
Availability of technical support to effectively use instructional technology in
my programs.
2.65 .84
Availability of age-appropriate instructional software 2.34 .77
My ability to integrate technology in my programs 2.25 .82
Administrative support for integration of technology in my programs 2.22 .89
Type of programs I facilitate 2.20 .74
Youth’s ability to use technology in my programs 2.08 .79
Composite Mean: 2.38 .56
Note. (N = 122). 1 = Not a barrier, 2 = Minor barrier, 3 = Moderate barrier, and 4 = Major
barrier. Scale interpretation: 1.00 to 1.49 = Not a barrier, 1.50 to 2.49 = Minor barrier, 2.50 to
3.49 = Moderate barrier, and 3.50 to 4.00 = Major barrier.
Research question four sought to determine what extent Extension youth educators
were experiencing anxiety toward using technology in youth development programming. The
items in this scale were measured using a five-point summated rating scale. The real limits of this
scale were 1.00 to 1.49 = No anxiety, 1.50 to 2.49 = Some anxiety, 2.50 to 3.49 = Moderate
anxiety, 3.50 to 4.49 = High anxiety, and 4.50 to 5.00 = Very high anxiety. The summated mean
for technology anxiety was 2.24 (SD = 1.03), demonstrating that Extension educators perceive
“Some anxiety” toward technology. Regarding Extension educators’ anxiety, all participants
indicated that they experience some anxiety toward using technology in youth development
programming. The highest rated statement (highest anxiety) was “How anxious do you feel when
you cannot keep up with new technology?” (M = 2.43, SD = .1.25) (see Table 4).
Research question five asked sought to determine what relationships existed between
Extension youth educators’ use of technology in the youth development programming and the
following variables: perception of barriers to using technology, level of perceived anxiety toward
technology, and selected personal and professional characteristics (i.e., age, gender, years of
professional experience, and sources of technology training). The data analysis revealed one
statistically significant relationship (see Table 5). The variable technology use had a positive and
moderate association with technology availability.
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Table 4
Anxiety Perceived by Extension Youth Educators toward Using Technology in Youth
Development Programming
Item M SD
How anxious do you feel when you cannot keep up with new technology? 2.43 1.25
How anxious do you feel when you attempt to use new options on various
technologies?
2.31 1.07
How anxious do you feel when you are faced with using new technology? 2.31 1.17
How anxious do you feel when someone uses a technology term that you do not
understand?
2.23 1.11
How anxious do you feel when you fear you may break or damage the technology
you are using?
2.22 1.25
How anxious do you feel when you try to learn technology related skills? 2.11 1.11
How anxious do you feel when you think about your technology skills compared
to the skills of other Extension youth educators?
2.10 1.14
Composite Mean: 2.24 1.03
Note. (N = 122). 1 = No anxiety, 2 = Some anxiety, 3 = Moderate anxiety, 4 = High anxiety, and 5
= Very high anxiety. Scale interpretation: 1.00 to 1.49 = No anxiety, 1.50 to 2.49 = Some anxiety,
2.50 to 3.49 = Moderate anxiety, 3.50 to 4.49 = High anxiety, and 4.50 to 5.00 = Very high
anxiety.
Table 5
Relationships between Extension Educators’ Technology Use, Technology Availability, Perceived
Barriers, Anxiety and Personal and Professional Characteristics
Variable r p Effect size
interpretation
Technology availability .46c <.001 Moderate
Barriers -.18a .052 N/A
Technology training .16a .075 N/A
Anxiety -.14a .117 N/A
Gender .04b .678 N/A
Age .04a .663 N/A
Years of experience -.02a .795 N/A
Note. (N = 122). aPearson Product Moment correlation. bPoint bi-serial correlation. cThe strength of relationships
was determined using Davis’ (1971) coefficient conventions: r = .00 to .09 = Negligible, r = .10
to .29 = Low, r = .30 to .49 = Moderate, r = .50 to .69 = Substantial, and r ≥ .70 = Very Strong.
Research question six sought to determine if selected variables explain a statistically
significant proportion of the variance in technology use by youth educators. The dependent
variable in the forward regression analysis was the technology use scale mean. The potential
explanatory variables were barriers, anxiety, technology availability, age, gender, years of
professional experience, and sources of technology training. One variable, technology
availability (R2 = .21), explained a medium amount of the variance in technology use (Cohen,
1988). However, barriers, anxiety, age, gender, years of experience, and sources of technology
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Journal of Agricultural Education 27 Volume 55, Issue 2, 2014
training did not enter the model. The overall model represents a medium effect size (Cohen,
1988) (see Table 6).
Table 6
Forward Multiple Regression Model Summary for Technology Use in Youth Development
Programming
Source SS df MS F P
Regression 7.82 1 7.82 31.5 <.001
Residual 29.76 120 .25
Total 37.58 121
Variable R R2
Adjusted
R2 SE
Change Statistics
R2
change
F
change
P of F
change
Technology
availability
.46 .21 .20 .50 .21 31.55 <.001
Note. (N = 122). Dependent Variable: Technology Use. Predictor: Technology Availability.
Conclusions
The conclusions are limited to Extension youth development educators in the three states
studied; namely, Louisiana, Mississippi, and Tennessee.
Extension youth educators in the three states range in age from 23 to 61 years with an
average of 39 years; almost two-thirds are female. The educators have an average of 11 years of
experience as youth development educators. According to Bennett et al. (2008), digital
immigrants are those who were born prior to 1980 and who lack fluency in the technological
language of their counterparts. Therefore, it was concluded that a majority of the Extension
educators are digital immigrants and were self-taught regarding technology training, consistent
with Bennett et al. (2008) and Prensky (2001a, 2001b) who stated that digital immigrants must be
willing to learn how to use technology to reach digital natives.
Extension educators in the three states are using some technology in youth development
programming, directly supporting previous studies on technology use in the teaching/learning
process (Kotrlik & Redmann, 2005, 2009; Kotrlik et al., 2003). More than three-fourths of
Extension educators are using Facebook which is consistent with conclusions from a study on
social media use by Extension educators by O’Neill et al. (2011) who stated that a majority of
Extension educators were using Facebook. However, less than one fourth of Extension educators
are using Twitter, contradicting conclusions by O’Neill et al. (2011) who found that more than
half of Extension educators were using Twitter in youth development programming.
Extension educators perceive that only minor barriers exist to using technology in youth
development programming. This conclusion supports previous studies by Kotrlik and Redmann
(2005, 2009), Kotrlik et al. (2003), and Redmann and Kotrlik (2004) that concluded that
agricultural, career and technical education, and adult education teachers perceive no substantial
barriers to using technology in the learning process. Extension educators experience some
anxiety toward using technology in youth development programming which is consistent with the
findings of studies of career and technical education teachers’ technology anxiety (Kotrlik &
Redmann, 2005, 2009; Redmann & Kotrlik, 2004). Having technology available for use explains
a medium amount of the technology use among Extension educators
(Cohen, 1988) which is consistent with Kotrlik and Redmann’s (2009) study of career and
technical educators. Perceived barriers, anxiety, age, gender, years of experience, and sources of
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Journal of Agricultural Education 28 Volume 55, Issue 2, 2014
technology training do not explain Extension youth educators technology use in their
programming. This is also consistent with Kotrlik and Redmann’s (2009) study of career and
technical education teachers in which they found that gender, sources of technology training and
years teaching experience did not explain technology use in instruction.
Implications
Extension educators see value in using various technologies in programming. However,
Extension educators are not using technology to a great degree. Technologies such as text
messaging, smart phones, and Facebook are used by a majority of Extension youth educators, yet
less than half are using digital video cameras, tablets, and Twitter. Perhaps, that could be
explained by an abundance of Extension educators who are digital immigrants and possibly less
likely to use technology to a great degree and even less educated on the use of technology than
their counterparts in other educational fields. Further, Extension educators may perceive some
youth programming opportunities as being taught better and more efficiently without the use of
technology. Consistent with Rogers’ (2003) theory, Extension educators may be more willing to
adopt more technologies if they are provided with opportunities for trialability and observability.
Recommendations
It is essential for Extension youth educators to use technology in their programming and
to reach out to more youth as the youth they serve are part of the digital native generation
(Prensky, 2001a, 2001b). Therefore, Extension youth educators should seek further training
opportunities to become more proficient in incorporating technology in youth development
programming. Further, Extension administration has a role in providing workshops and other
training opportunities for Extension educators to learn to incorporate technology into their
programming. Because the strongest perceived barrier to using technology in youth development
programming is having technology available to youth, Extension youth educators and Extension
administration should strive to acquire appropriate technology for use in Extension youth
programming.
Further research is warranted on Extension youth educators from other states to better
understand the extent of their technology use in youth development programming across the
nation. Additional research addressing in which program areas educators are using technology
would provide researchers a better understanding of training opportunities needed to expand
technology use across youth development programming. A study should be conducted that
compares youth satisfaction of 4-H programming in programs where technology is heavily used
versus programs where technology is not heavily used.
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Journal of Agricultural Education 29 Volume 55, Issue 2, 2014
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